library(diprate)
library(ggplot2)
library(viridis)
Loading required package: viridisLite
library(tidyr)
library(ggridges)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

Function to quantify ppERK using both proportion of positive cells and the avg intensity

Using both proportion of cells staining positively for ppERK and the integrated values of the positive cells.

ppERKint <- function(dat, signif=4) {
    cell_count <- nrow(dat)
    pos_cells <- nrow(dat[dat$positive.w2==1,])
    val <- pos_cells/cell_count*sum(dat[dat$positive.w2==1,]$avg.pERK)
    val <- signif(val,signif)
    return(val)
}

Load data

d <- read.csv("https://www.dropbox.com/scl/fi/mty3qbhtu917pbrvra5kj/202402119_singlecell_all_cpa.csv?rlkey=9fmn41fu22p1l0lpkwf1d21nw&dl=1")

QC

Removing cells with total area > 4500 and < 100 \(pixels^2\)
Removing objects with integrated intensity > 5e6 because they tend to be bubbles, precipitated antibody, or debris. It was found that setting minimum area to above 200 pixels^2 predominantly filtered out data from the Ca2+ free control group. Because of this bias in filtering, minimum area was set to > 100 pixels.

o_nrow <- nrow(d)
d <- d[d$total.area < 4500 & d$total.area > 50 & d$w2.stain.integr.intens <5e6,]
message(cat("Current nrow = ",nrow(d),"\nOriginal nrow = ",o_nrow))
Current nrow =  668807 
Original nrow =  683159
message(cat("QC removed",signif(100-(nrow(d)/o_nrow*100),2),"% of cells"))
QC removed 2.1 % of cells

drug2.conc missing in data

Had to remove from keepcols

keepcols <- c("cell.line","drug1","drug1.conc","drug2","well",
              "BRAFi.tx.time","iono.tx.time",
              "cell.id","total.area","positive.w2","w2.stain.area","w2.stain.integr.intens", "avg.pERK")

d <- d[,keepcols]
ab_ctrl_wells <- unique(d[d$drug1=="" & d$drug2=="",]$well)
untx_ctrl_wells <- unique(d[d$drug2=="control",]$well)
ctrl_wells <- c(ab_ctrl_wells,untx_ctrl_wells)

d[is.na(d$drug1.conc),"drug1.conc"] <- 0
d[is.na(d$drug2.conc),"drug2.conc"] <- 0

d[d$drug1=="","drug1"] <- "control"

tx <- tibble(d[!d$well %in% ctrl_wells, ])
tx <- tx[order(tx$BRAFi.tx.time),]

rownames(tx) <- NULL

ctrls <- d[d$well %in% ctrl_wells, ]

tx$drug1.conc <- signif(tx$drug1.conc, digits= 2)
tx$drug1 <- factor(tx$drug1)

Controls

  • drug1 == "holiday": BRAFi-treated cells given a “holiday”; replaced medium with FBS-free, undrugged media
  • drug1 == "persistent": BRAFi maintained, no agonist added
  • drug1 == "control": Drug-naive cells in FBS free medium (no PLX or ionomycin ever added)
  • drug1 == "controlFBS": Drug-naive cells in FBS-containing medium
  • drug1 == "" & drug2 == "": Antibody controls (1° and 2°)
ctrlType <- function(drug1)
    switch(drug1,
           controlFBS="naive_wFBS",
           control="naive_woFBS",
           drug1
           )


d$ctrl_type <- sapply(d$drug1, function(x) 
    ifelse(x %in% c("","control","controlFBS","holiday","persistent"),ctrlType(x),""))

d[d$drug1=="" & is.na(d$drug2),"ctrl_type"] <- "ab_ctrl"
d[d$ctrl_type=="","ctrl_type"] <- NA
d$ctrl_type <- factor(d$ctrl_type)
unique(d$ctrl_type)
[1] naive_woFBS naive_wFBS  <NA>        persistent 
Levels: naive_wFBS naive_woFBS persistent
dtp <- d[!is.na(d$ctrl_type),]
ggplot(dtp, aes(x = avg.pERK, y = ctrl_type, fill = after_stat(x))) + 
    xlim(-1,1.5) + 
    geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
    scale_fill_viridis_c(name = "avg ppERK", option = "C") +
    labs(title = 'Controls', 
         subtitle="") + 
    xlab("Mean ppERK/cell (A.U.)") + ylab("Control condition")

tx <- d[is.na(d$ctrl_type),]
drugs <- unique(tx$drug1)
ppdf <-tx %>%
    group_by(BRAFi.tx.time, drug1, drug1.conc, iono.tx.time, positive.w2) %>%
    summarise(num= n()) 
`summarise()` has grouped output by 'BRAFi.tx.time', 'drug1', 'drug1.conc', 'iono.tx.time'. You can override using the `.groups` argument.
ppdf$positive.w2 <- rep(c("negative","positive"), nrow(ppdf)/2)

pct.pos <- ppdf %>%
    spread(positive.w2, num) %>%
    mutate(total= negative+positive) %>%
    summarise(propos= positive/total) %>%
    ungroup()
`summarise()` has grouped output by 'BRAFi.tx.time', 'drug1', 'drug1.conc'. You can override using the `.groups` argument.
dat <- tx[tx$drug1=="Ionomycin" & tx$BRAFi.tx.time=="3 days",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="3 days" & pct.pos$drug1== "Ionomycin",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-tolerant cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
[[1]]

dat <- tx[tx$drug1=="Ionomycin" & tx$BRAFi.tx.time=="60 min",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="60 min"  & pct.pos$drug1== "Ionomycin",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-sensitive cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
[[1]]

dat <- tx[tx$drug1=="CPA" & tx$BRAFi.tx.time=="3 days",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="3 days" & pct.pos$drug1== "CPA",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-tolerant cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

dat <- tx[tx$drug1=="CPA" & tx$BRAFi.tx.time=="60 min",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="60 min"  & pct.pos$drug1== "CPA",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-sensitive cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

---
title: "R Notebook"
output: html_notebook
---



```{r Setup}
library(diprate)
library(ggplot2)
library(viridis)
library(tidyr)
library(ggridges)
library(dplyr)
```

### Function to quantify ppERK using both proportion of positive cells and the avg intensity
Using both proportion of cells staining positively for ppERK and the integrated values of the positive cells.  

```{r}
ppERKint <- function(dat, signif=4) {
    cell_count <- nrow(dat)
    pos_cells <- nrow(dat[dat$positive.w2==1,])
    val <- pos_cells/cell_count*sum(dat[dat$positive.w2==1,]$avg.pERK)
    val <- signif(val,signif)
    return(val)
}
```

## Load data
```{r}
d <- read.csv("https://www.dropbox.com/scl/fi/mty3qbhtu917pbrvra5kj/202402119_singlecell_all_cpa.csv?rlkey=9fmn41fu22p1l0lpkwf1d21nw&dl=1")
```


# QC
Removing cells with total area > 4500 and < 100 $pixels^2$  
Removing objects with integrated intensity > 5e6 because they tend to be bubbles, precipitated antibody, or debris.
It was found that setting minimum area to above 200 pixels^2 predominantly filtered out data from the Ca2+ free control group. Because of this bias in filtering, minimum area was set to > 100 pixels. 

```{r}
o_nrow <- nrow(d)
d <- d[d$total.area < 4500 & d$total.area > 50 & d$w2.stain.integr.intens <5e6,]
message(cat("Current nrow = ",nrow(d),"\nOriginal nrow = ",o_nrow))
message(cat("QC removed",signif(100-(nrow(d)/o_nrow*100),2),"% of cells"))
```

### `drug2.conc` missing in data
Had to remove from `keepcols`
```{r}
keepcols <- c("cell.line","drug1","drug1.conc","drug2","well",
              "BRAFi.tx.time","iono.tx.time",
              "cell.id","total.area","positive.w2","w2.stain.area","w2.stain.integr.intens", "avg.pERK")

d <- d[,keepcols]
```


```{r}
ab_ctrl_wells <- unique(d[d$drug1=="" & d$drug2=="",]$well)
untx_ctrl_wells <- unique(d[d$drug2=="control",]$well)
ctrl_wells <- c(ab_ctrl_wells,untx_ctrl_wells)

d[is.na(d$drug1.conc),"drug1.conc"] <- 0
d[is.na(d$drug2.conc),"drug2.conc"] <- 0

d[d$drug1=="","drug1"] <- "control"

tx <- tibble(d[!d$well %in% ctrl_wells, ])
tx <- tx[order(tx$BRAFi.tx.time),]

rownames(tx) <- NULL

ctrls <- d[d$well %in% ctrl_wells, ]

tx$drug1.conc <- signif(tx$drug1.conc, digits= 2)
tx$drug1 <- factor(tx$drug1)
```

### Controls 
* `drug1 == "holiday"`: BRAFi-treated cells given a "holiday"; replaced medium with FBS-free, undrugged media
* `drug1 == "persistent"`: BRAFi maintained, no agonist added
* `drug1 == "control"`: Drug-naive cells in FBS free medium (no PLX or ionomycin ever added)
* `drug1 == "controlFBS"`: Drug-naive cells in FBS-containing medium
* `drug1 == "" & drug2 == ""`: Antibody controls (1° and 2°) 

```{r}
ctrlType <- function(drug1)
    switch(drug1,
           controlFBS="naive_wFBS",
           control="naive_woFBS",
           drug1
           )


d$ctrl_type <- sapply(d$drug1, function(x) 
    ifelse(x %in% c("","control","controlFBS","holiday","persistent"),ctrlType(x),""))

d[d$drug1=="" & is.na(d$drug2),"ctrl_type"] <- "ab_ctrl"
d[d$ctrl_type=="","ctrl_type"] <- NA
d$ctrl_type <- factor(d$ctrl_type)
unique(d$ctrl_type)
```

```{r}
dtp <- d[!is.na(d$ctrl_type),]
```

```{r message=FALSE, warning=FALSE}
ggplot(dtp, aes(x = avg.pERK, y = ctrl_type, fill = after_stat(x))) + 
    xlim(-1,1.5) + 
    geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
    scale_fill_viridis_c(name = "avg ppERK", option = "C") +
    labs(title = 'Controls', 
         subtitle="") + 
    xlab("Mean ppERK/cell (A.U.)") + ylab("Control condition")

```



```{r}
tx <- d[is.na(d$ctrl_type),]
drugs <- unique(tx$drug1)
```

```{r}
ppdf <-tx %>%
    group_by(BRAFi.tx.time, drug1, drug1.conc, iono.tx.time, positive.w2) %>%
    summarise(num= n()) 


ppdf$positive.w2 <- rep(c("negative","positive"), nrow(ppdf)/2)

pct.pos <- ppdf %>%
    spread(positive.w2, num) %>%
    mutate(total= negative+positive) %>%
    summarise(propos= positive/total) %>%
    ungroup()
```


```{r message=FALSE, warning=FALSE}
dat <- tx[tx$drug1=="Ionomycin" & tx$BRAFi.tx.time=="3 days",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="3 days" & pct.pos$drug1== "Ionomycin",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-tolerant cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
```
```{r message=FALSE, warning=FALSE}
dat <- tx[tx$drug1=="Ionomycin" & tx$BRAFi.tx.time=="60 min",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="60 min"  & pct.pos$drug1== "Ionomycin",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-sensitive cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
```


```{r message=FALSE, warning=FALSE}
dat <- tx[tx$drug1=="CPA" & tx$BRAFi.tx.time=="3 days",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="3 days" & pct.pos$drug1== "CPA",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-tolerant cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g



dat <- tx[tx$drug1=="CPA" & tx$BRAFi.tx.time=="60 min",]


g <- lapply(unique(dat$drug1.conc), function(dc) {
    dtp <- dat[dat$drug1.conc==dc,]
    dtp <- dtp[order(dtp$iono.tx.time),]
    
    n_vals <- pct.pos[pct.pos$drug1.conc==dc & pct.pos$BRAFi.tx.time =="60 min"  & pct.pos$drug1== "CPA",]
    n_vals$propos <- signif(n_vals$propos*100, 3)
    
    ppERK_vals <- sapply(unique(dtp$iono.tx.time), function(tt) ppERKint(dtp[dtp$iono.tx.time==tt,]))
    names(ppERK_vals) <- unique(dtp$iono.tx.time)
    ppERK_vals <- round(ppERK_vals/ppERK_vals[names(ppERK_vals)=="1"],2)
    
    out <- ggplot(dtp, aes(x = avg.pERK, y = factor(iono.tx.time), fill = after_stat(x))) + 
        xlim(-1,1.5) + 
        geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
        scale_fill_viridis_c(name = "avg ppERK", option = "C") +
        labs(title = 'Drug-sensitive cells', 
             subtitle=paste(unique(dtp$drug1),dc)) + 
        xlab("Mean ppERK/cell (A.U.)") + ylab("Time after Tx") + 
        annotate("text", x=1.5, y=1:(length(ppERK_vals))+0.25, label=ppERK_vals, size=2.5) +
        
        
        annotate("text", x=1.5, y=1.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==1,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=2.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==5,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=3.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==10,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=4.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==15,"propos"]), size=2.5) + 
        
        annotate("text", x=1.5, y=5.75, label= 
                     paste("%pos =",n_vals[n_vals$iono.tx.time==20,"propos"]), size=2.5) +
        annotate("text", x=1.5, y=1.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==1,])), size=2.5) + 
        annotate("text", x=1.5, y=2.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==5,])), size=2.5) + 
        annotate("text", x=1.5, y=3.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==10,])), size=2.5) + 
        annotate("text", x=1.5, y=4.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==15,])), size=2.5) + 
        annotate("text", x=1.5, y=5.5, label= paste("n =",nrow(dtp[dtp$iono.tx.time==20,])), size=2.5) 

    out
})

g
```




